Browse > Article
http://dx.doi.org/10.7843/kgs.2022.38.3.35

Deep Learning based Estimation of Depth to Bearing Layer from In-situ Data  

Jang, Young-Eun (Innovative SMR System Development Division, Korea Atomic Energy Research Institute)
Jung, Jaeho (Innovative SMR System Development Division, Korea Atomic Energy Research Institute)
Han, Jin-Tae (Dept. of Geotechnical Engrg. Research, Korea Institute of Civil Engrg. & Building Technology)
Yu, Yonggyun (Artificial Intelligence Application & Strategy Team, Korea Atomic Energy Research Institute / Dept. of Nuclear and Radiation Safety, UST)
Publication Information
Journal of the Korean Geotechnical Society / v.38, no.3, 2022 , pp. 35-42 More about this Journal
Abstract
The N-value from the Standard Penetration Test (SPT), which is one of the representative in-situ test, is an important index that provides basic geological information and the depth of the bearing layer for the design of geotechnical structures. In the aspect of time and cost-effectiveness, there is a need to carry out a representative sampling test. However, the various variability and uncertainty are existing in the soil layer, so it is difficult to grasp the characteristics of the entire field from the limited test results. Thus the spatial interpolation techniques such as Kriging and IDW (inverse distance weighted) have been used for predicting unknown point from existing data. Recently, in order to increase the accuracy of interpolation results, studies that combine the geotechnics and deep learning method have been conducted. In this study, based on the SPT results of about 22,000 holes of ground survey, a comparative study was conducted to predict the depth of the bearing layer using deep learning methods and IDW. The average error among the prediction results of the bearing layer of each analysis model was 3.01 m for IDW, 3.22 m and 2.46 m for fully connected network and PointNet, respectively. The standard deviation was 3.99 for IDW, 3.95 and 3.54 for fully connected network and PointNet. As a result, the point net deep learing algorithm showed improved results compared to IDW and other deep learning method.
Keywords
Spatial Interpolation; IDW; Deep learning network; PointNet; Fully connected network; Depth to Bearing Layer;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Kim, A.R., Kim, D., B. Y.S., and Lee, S.W. (2018), "Crack Detection of Concrete Structure Using Deep Learning and Image Processing Method in Geotechnical Engineering", Journal of the korean geotechnical society, Vol.34, No.12, pp.145-154.   DOI
2 Ahn, J.M. and Park, I.H. (2012), "An Assessment on the Hydraulic Characteristics of a Multi-dimensional Model in Response to Measurement Resolution and Spatial Interpolation Methods", Journal of Korean Society for Geospatial Information Science, Vol. 20, No.1, pp.43-51.   DOI
3 Alobaidi, M.H., Meguid, M.A., and Chebana, F. (2019), "Predicting seismic-induced liquefaction through ensemble learning frameworks", Scientific Reports, Vol. 9, No.11786.
4 Bargaoui, Z.K. and Chebbi, A. (2009), "Comparison of Two Kriging Interpolation Methods Applied to Spatiotemporal Rainfall", Journal of Hydrology, Vol.365, No.1-2, pp.56-73.   DOI
5 Gul, E. and Ersahin, S. (2019), "Evaluating the Desertification Vulnerability of a Semiarid Landscape under Different Land uses with the Environmental Sensitivity Index", Land Degradation & Development, Vol.30, No.7, pp.811-823.   DOI
6 Korea Expressway Corporation (2012), Expressway construction guide specification (in Korean).
7 Navfac DM7 (1974), Design Manual-Soil Mechanics Foundations, and Earth Structures, U.S.Government Printing Office, Wahingthon, D.C.
8 MOLIT (2016), Foundation Design Criteria, Ministry of Land, Infrastructure and Transport (in Korean).
9 Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2019), "Pointnet: Deep learning on point sets for 3d classification and segmentation", In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.652-660.
10 Seo, J.H., Sohn, J.R., and Mo, R. (2017), "A Study on Exposure Assessment for Fine Dust by Using Kriging Method: The Case of Seoul Metropolitan City", In AGU Fall Meeting Abstracts, Vol.2017, pp.A43A-2420.
11 You, Hojuna, Kim, Dongsua (2017), "Development of an Anisotropic Spatial Interpolation Method for Velocity in Meandering River Channel", J. Korea Water Resour. Assoc., Vol.50, No.7, pp.455-465.   DOI